The DEGGs package test for differential gene-gene correlations across different groups of samples in count data from high-throughput sequencing assays.
Specific gene-gene interactions can be explored and gene-gene pair regression plots can be interactively shown.

To install from Github please use the following on your R console
devtools::install_github("elisabettasciacca/DEGGs", build_vignettes = TRUE)

Load package and sample data
library(DEGGs) data("BRCA_metadata") data("BRCA_normCounts")

Generate specific gene-gene networks for each subtype
subnetworks_object <- generate_subnetworks(normalised_counts = BRCA_normCounts, metadata = BRCA_metadata, subgroup_variable = "SUBTYPE", subgroups = c("BRCA_Her2", "BRCA_LumA"), entrezIDs = TRUE, convert_to_gene_symbols = TRUE, cores = 2)

Visualise
View_interactive_subnetwork(subnetworks_object)

Get a table listing all the significant gene-gene interactions found in each subtype
extract_sig_deggs(subnetworks_object)

Print differential regression fits for a single gene-gene interaction through the print_regressions function
print_regressions(gene_A = "NOTCH2", gene_B = "DTX4", deggs_object = subnetworks_object, legend_position = "bottomright")

DEGGs was developed by Elisabetta Sciacca and supported by the bioinformatics team at Experimental Medicine & Rheumatology department and Centre for Translational Bioinformatics (Queen Mary University London), in joint collaboration with the Department of Clinical and Experimental Medicine at University of Catania.

If you use this package please cite as:

citation("DEGGs")

or:

Sciacca, Elisabetta, et al. "DEGGs: an R package with shiny app for the identification of differentially expressed gene–gene interactions in high-throughput sequencing data." Bioinformatics 39.4 (2023): btad192.